Course:EOSC311/2025/Using Computer Science to Decode Earthquakes

Summary

Earthquakes are among the most devastating natural hazards, yet predicting their exact occurrence remains elusive. As a Computer Science student, I’ve always been fascinated by how data and algorithms can reveal patterns hidden in chaos. This project is about the geological foundations of earthquake activity and how computer science contributes to understanding and responding to seismic hazards. It explores the key tectonic environments where earthquakes originate, such as subduction zones, transform faults, and mid-ocean ridges. It outlines the scientific reasons why earthquakes remain inherently unpredictable. The project highlights what can be forecasted through long-term seismic hazard mapping, aftershock estimation, and early warning systems. It also discusses how geologists collect critical seismic and deformation data using GPS networks, InSAR, and seismographs, and how computer science tools, particularly machine learning and statistical modeling are applied to analyze this data and identify patterns in seismic behavior. Overall, this project emphasizes the importance of interdisciplinary collaboration between geology and computer science to improve earthquake resilience and risk mitigation strategies.

Statement of connection and why I chose it

I chose to explore the intersection of geology and computer science because both fields seek to understand and model complex systems. Geologists analyze tectonic movements that shape the Earth’s surface, while computer scientists build models to find structure in seemingly random data. In earthquake research, the two disciplines come together in a powerful way, geology provides the observational data, and CS helps transform that data into actionable insights. This project allowed me to apply what I’ve learned in machine learning to a real-world, high-impact application. I’ve tried to break down the core concepts of earthquakes and how they relate to computer science in a way that’s simple and easy for anyone to grasp.

Understanding Earthquakes

Tectonic Plates and Earthquake Zones

Understanding Tectonic plates

Earthquakes primarily occur along tectonic plate boundaries, where immense geological forces are constantly at work. There are three major types of boundaries:

• Subduction Zones: One plate dives beneath another. These are responsible for the most powerful earthquakes (e.g., 2011 Tōhoku earthquake in Japan).
• Transform Faults: Plates slide past each other horizontally. A well-known example is the San Andreas Fault in California.
• Mid-Ocean Ridges: Plates move apart, and magma rises to form new crust, often causing underwater earthquakes.

Understanding these zones is the foundation of seismic risk assessment and early warning systems.

What Causes Earthquakes?

Earthquakes are caused by the sudden release of energy along faults in the Earth’s crust, where tectonic plates meet and interact. These massive plates are in constant motion due to convection currents in the mantle beneath them. Over time, stress accumulates at plate boundaries, and when the stress exceeds the strength of the rocks, it is released as seismic waves, causing the ground to shake.

Why Earthquakes Can’t Be Predicted?

Despite decades of research, precise earthquake prediction is not currently possible. Fault systems are highly complex, and there are no consistent or observable precursors that reliably signal when and where an earthquake will strike. Unlike hurricanes or volcanic eruptions, earthquakes often occur with little or no warning.

This unpredictability stems from:

• The chaotic nature of stress accumulation and release along faults.
• Limited visibility into what’s happening deep underground.
• Incomplete or noisy historical data.

What Can Be Predicted then?

Although predicting exact events remains out of reach, researchers can still estimate probabilities and likely outcomes based on geological history and statistical models:

EEWS (Japan)

• Seismic Hazard Mapping: Long-term maps based on fault data, rock types, and past earthquakes estimate the probability of shaking in a region over decades.

• Aftershock Forecasting: After a major quake, models can estimate where and how strong subsequent shocks may be.
• Earthquake Early Warning Systems (EEWS): Systems like ShakeAlert (U.S.) and J-Alert (Japan) detect initial (P) waves (basically non destructive seismic waves) and send warnings seconds before the more destructive S-waves arrive, buying valuable time to take cover or stop transit systems.

Earthquake Risk and Monitoring

Given what we can forecast, a wide range of technologies are used to monitor and assess seismic activity in near-real-time:

A seismograph

• GPS Networks (like the Plate Boundary Observatory): Monitor crustal movement, fault creep, and strain accumulation.
• InSAR (Interferometric Synthetic Aperture Radar): Satellite imaging technique that detects subtle ground deformation—often invisible to the naked eye.
• Seismographs: These instruments are deployed globally and measure the intensity, timing, and location of seismic events.

The Role of Computer Science in Earthquake Research

This is where my discipline comes in. Computer science allows us to make sense of the vast, complex, and noisy data generated by these monitoring systems. Here are some of the ways CS is shaping earthquake science:

Data Collection and Preprocessing

Before any predictions can be made, we need data! Earthquake data comes from sensors placed all over the world, which measure shaking, ground movement, and the energy released during seismic events. But this raw data is often messy or incomplete.

Computer scientists help by writing programs that can:

• Download seismic data from organizations like the US Geological Survey (USGS) or IRIS.
• Clean and organize that data so it can be easily analyzed. For example, we might remove repeated entries, correct time zones, or convert the format.
• Merge data from different sources, such as ground sensors, satellites, and historical records.

Think of it like organizing a massive library: CS helps sort, label, and shelve the information so it’s ready to use.

Machine Learning for Earthquake Analysis

Once the data is ready, we use machine learning—an area of CS that allows computers to “learn” patterns from past data without being explicitly programmed for every situation.

Here’s how it helps with earthquakes:

• Aftershock prediction: After a major earthquake, smaller quakes (aftershocks) usually follow. Machine learning models like LSTM (Long Short-Term Memory) can look at a timeline of past quakes and learn patterns to guess when and where the next aftershock might happen.
• Random Forest models: These use hundreds of small decision rules (like "If the earthquake was shallow, go this way; if deep, go that way") to make more accurate predictions about future events.

In simple terms, machine learning models act like advanced weather apps for earthquakes—but instead of temperature, they forecast seismic activity.

Seismic Clustering and Fault Detection

Not all fault lines are visible on the surface, some lie hidden underground. To discover these, scientists look for small quakes that occur in clusters.

An image depicting DBSCAN clustering

Computer science helps by using clustering algorithms, which group together events that are close in space and time:

• One example is DBSCAN, a tool that looks for “clumps” of earthquakes in a map, ignoring outliers.
• These clusters might reveal the presence of a new fault line or show that stress is building in a certain area.

This is like drawing invisible lines on a map by letting the data show where earthquakes “like” to occur together.

Earthquake Early Warning Systems (EEWS)

Even if we can’t predict earthquakes long in advance, we can detect them right as they start and send out warnings seconds before the worst shaking hits.

Here’s how CS supports these systems:

• Signal detection algorithms monitor real-time seismic waves. When a small, fast-moving wave (P-wave) is detected, the system races to alert people before the slower, more destructive wave (S-wave) arrives.
• Software systems instantly calculate how strong the shaking might be and which areas will be affected.
• These warnings can be sent to phones, trains, elevators, and hospitals, giving people precious seconds to protect themselves.

While a few seconds might not sound like much, it can be the difference between safety and danger, especially for trains, surgeries, or classrooms.

Challenges and Ethical Considerations

Like any technology, earthquake prediction tools have limitations and ethical concerns:

• Data bias: Earthquake data is better in developed countries. Models trained on this data might not work as well in poorer regions.
• Transparency: Models should be understandable to geologists and not just “black boxes.” Human oversight is crucial.
• Open Access: Sharing code and data openly allows scientists from all countries to build better systems and help save more lives.

Computer scientists must work closely with geologists, governments, and local communities to ensure that their tools are fair, transparent, and useful for everyone.

Conclusion

This project has shown how deeply connected geology and computer science can be, especially in the context of earthquake science. While earthquakes originate from natural geological forces, it is through computational tools that we analyze their patterns, assess risks, and build systems to respond in real time. Geology provides the foundational understanding of tectonic processes, while computer science enhances our ability to model uncertainty, visualize hazards, and deliver timely warnings. Even though precise prediction remains out of reach, the combined efforts of these fields allow us to forecast probabilities, map vulnerabilities, and ultimately reduce risk to society.

Personal Reflection

This project deepened my appreciation for both disciplines. I saw how geological knowledge provides critical context and constraints, while computer science offers the tools to build flexible, adaptive, and predictive models.

I particularly enjoyed exploring the different types of machine learning models used in seismology, each suited to different patterns and forecasting needs. From time series models like LSTM to clustering techniques like DBSCAN, it was exciting to see how methods I’ve learned in computer science are being applied in the real world to address such an impactful challenge. While it felt a bit too "techy" to include full implementations in this report, I’ve linked some of the GitHub repositories I found most interesting in the references section for those who may want to explore them further.

This interdisciplinary exploration has shown me that the boundary between Earth science and data science and computer science is full of opportunity, and I hope to continue learning.

References

This Earth Science resource was created by Course:EOSC311.